where’s that fu*#}ng wi-fi ?!?...

Eduard Garcia-Villegas

Dept. of Network [email protected]

Just some common sense rules put together in a nice set of

colorful slides

EFFICIENT Wi-Fi deployments

The basics

Where’s that

fu*#}ng Wi-Fi ?!?

Contents

EFFICIENT Wi-Fi deployments

o Big vs. small

o Analyze requirements

o #STAs and #Needed radios

o Available channels

o Reuse factor

o Dimensioning cells

o Optimization

Efficient Wi-Fi deployments 2

by Podere Casanova

https://www.flickr.com/photos/podere-casanova/

Wi-Fi deployments: intro


In the era of ubiquitous Internet…

Wireless internet access can be a traumatic experience due too Many concurrent users (dense scenarios)

o Coexistence (older/slower devices, other technologies sharing the band, etc.)

o …

o POOR DESIGN

Wi-Fi deployments: big vs. small (1)


Coverage-driven design

o In the past: maximize cell size && minimize costs

• Optimize AP location and increase cell size less APs

needed (lower cost)

• Problems:

– more devices per AP (lower per STA throughput)

» Reduced efficiency due to higher collision probability

5 STAs x AP <2 STAs x AP

= STA

VS.

= AP



= STA

= AP

LAME!




needed (lower cost)

• Problems:

– Longer distances AP STA mean worse signal quality and,

hence, more robust (slower) PHY rates are used

» Capacity of the whole cell is reduced

» Longer tx time more power consumed and more collisions



= STA

= AP

HIDDEN NODES!

CAN’T REACH ITS AP!




needed (lower cost)

• Problems:

– More hidden nodes more collisions

– Power mismatch: AP (high tx power) and STA (low tx power)

» STA can hear the AP, but the AP can't hear the STA

» If you want a big cell, increase the antenna gain, not the tx power!






needed (lower cost)

• It has problems in present (dense) deployments.

Other key aspects

o KPI requirements

o Client and AP capabilities

• Are modern ≥ 11n capable (how many antennas)? Coexistence with 11a/b/g? Dual band?

o Propagation phenomena

• Outdoor/indoor? APs mounted on ceiling, walls or floor?

o User density

Efficient Wi-Fi deployments

The basics

Analyze requirements

ANALYZE REQUIREMENTS

Per user Total

#STAs per

RADIO

#RADIOS

NEEDED

AVAILABLE

CHANNELS

REUSE

FACTOR

DIMENSION

CELLS

OPTIMIZE/

TROUBLESHOOT

Wi-Fi deployments: requirements


The first thing is to identify key performance indicators (KPI)

o Minimum bandwidth required to satisfy supported applications

o Maximum latency tolerated

o Expected Min-Avg-Max number of active devices

Examples (per-user requirements):

o School

• BW: <3Mbps (video

streaming; desktop/file sharing)

• Delay tolerance: low (video streaming; intranet login)

• Users: Min-Avg-Max = up to 30 per classroom

o Convention center (1500 att.)

• BW: <1 Mbps (web browsing;

e-mail)

• Delay tolerance: Medium

• Users: “educated guess”– 70% will connect Wi-Fi device

– 50% simultaneously

– 1500 x 0.70 x 0.5 = 525

Wi-Fi deployments: #STAs and radios

Efficient Wi-Fi deployments: the basics 10

Capacity-driven design (rule of thumb)

o Example 1 school (<3Mbps x 30 users per classroom):

• 20 STAs per AP each classroom served by two radios (two

APs or one dual band AP)

– Assume homogeneous (IT-controlled) 11n 2x2 devices

– Good signal quality (high rates available) STAs achieve

~80Mbps of net throughput (isolated)

– Allow future growth: AP utilization ≤ 75%

75/(100*3Mbps/80Mbps) = 20 STAs per AP

o Example 2 convention center (<1Mbps x 525 users)

• 32 STAs per AP 525/32 = 16 – 17 radios

– Assume heterogeneous (BYOD) devices

– Diverse signal quality STAs achieve ~40Mbps of net

throughput

– AP utilization ≤ 80% 80/(100*1Mbps/40Mbps) = 32 STAs/AP


Per user Total

#STAs per

RADIO

#RADIOS

NEEDED

AVAILABLE

CHANNELS

REUSE

FACTOR

DIMENSION

CELLS

OPTIMIZE/

TROUBLESHOOT


The basics

Available channels

Wi-Fi deployments: channels (1)


Capacity limited by the scarcity of available spectrum

o 2.4GHz ISM band

• Only three non-overlapping channels (1,6,11)

• Four (almost) non-overlapping channels (1,5,9,13) where available

Baseline capacity

Three channel scheme:

Baseline x3

Ch1Ch11

Ch6

Three channel scheme:

Baseline x3.05

Ch1

Ch11

Ch6Ch11

Four channel scheme:

Baseline x3.9

Ch1

Ch5

Ch9Ch13

Wi-Fi deployments: channels (2)


Capacity limited by the scarcity of available spectrum

o 2.4GHz ISM band

• Only three non-overlapping channels (1,6,11)

• Four (almost) non-overlapping channels (1,5,9,13) where available

– Not available in all regulatory domains (e.g. North Americas)

– Many devices default to Americas config. will see coverage

gaps in the areas served by APs in Ch13.

• Highly congested: coexistence with WPANs, cordless phones, baby monitors, microwave ovens…

o 5GHz ISM band

• 15-21 non overlapping channels in different sub bands

• Highly variable from one regulatory domain to another

– Some channels only for indoor use, others require DFS

– Different tx power limits …


Per user Total

#STAs per

RADIO

#RADIOS

NEEDED

AVAILABLE

CHANNELS

REUSE

FACTOR

DIMENSION

CELLS

OPTIMIZE/

TROUBLESHOOT


The basics

Reuse Factor

Wi-Fi deployments: reuse factor


#Radios Needed

Reuse Factor =

Available Channels

o If Reuse Factor ≤ 1 LUCKY YOU!

o Otherwise, each channel is shared among Reuse Factor APs INTERFERENCE!

• Minimize interference by.

– Carefully dimensioning cells

– Smart channel management


Per user Total

#STAs per

RADIO

#RADIOS

NEEDED

AVAILABLE

CHANNELS

REUSE

FACTOR

DIMENSION

CELLS

OPTIMIZE/

TROUBLESHOOT


The basics

Dimension the cell

Wi-Fi deployments: dimension cells (1)


What is the cell radius?

o Max distance at which frames can be decoded

• Pt is tx power

– Decreases with MCS (to avoid distortion)

• Sr is receiver sensitivity

– Increases with MCS

– Rr reception range

– d is the distance tx rx

– α is the path loss exponent

o Different radius depending on targetedMCS

𝑷𝒓 ≈𝑷𝒕

𝒅𝜶⟶ 𝑹𝒓 ≈

𝑷𝒕

𝑺𝒓

𝟏 𝜶

VERY FAST

SLOW

R1 R2 Rn



How to set cell radius for Wi-Fi small cells?

o Reduce AP’s tx power

• Reduces interference over other cells

• Avoids AP/STA power mismatch

• Reduces suitable rates

NOT SO

FAST

SLOW



How to set cell radius for Wi-Fi small cells?

o Reduce AP’s tx power

• Reduces interference over other cells

• Avoids AP/STA power mismatch

• Reduces suitable rates

o Increase min tx rate of the cell

• Reduces performance anomaly and allows higher average rate

– Avoid “sticky” STAs

• Possible unsupported devices– Accept, at least, 802.11b@11Mbps?

OUT!

NOT SO

FAST



BUT…interference goes beyond the cell edge

o Carrier Sense Range (Rc)

• Max distance at which frame preamble can be detected and, hence, prevent concurrent transmissions in the same channel.

– Only 3dB SNR is enough! (>200m outdoors)

– Behavior improved in IEEE 802.11ax

o Beyond Carrier Sense Range

• Transmitted frames are just noiseLEAVE ME

ALONE!



Coverage strategy for maximal densification

o Reduce reuse distance

• Low gain directional antennas

• AP placement

– Overhead: AP installed on the ceiling/lamp posts facing down

– Side: AP installed on walls/pillars

– Floor: under floor/under seat (stadiums or auditoriums)

– Even consider mounting APs behind walls/obstacles and avoid LoS(enriches multipath diversity leveraged by MIMO)

120º

vs.

60º

co

ve

rag

e

reu

se

Ch1 Ch1Ch1Ch1

reducedreuse distance


Per user Total

#STAs per

RADIO

#RADIOS

NEEDED

AVAILABLE

CHANNELS

REUSE

FACTOR

DIMENSION

CELLS

OPTIMIZE/

TROUBLESHOOT


The basics

Finishing touches

Wi-Fi deployments: channel plan (1)


Ch11

Ch11

Ch6

Ch1

Ch6

Ch11

Ch1

Ch6

Ch11

Ch1

In your

dreamsReality(t)

Dynamic and unpredictable spectrum utilization

o License-free bands!

Intelligent channel assignments are required



NO INTERFERENCE!

Automatic and dynamic channel assignments aimed at reducing interference maximizing performance

o APs gather information of the environment

• Number of APs detected

• Power received from neighboring APs

• Portion of time the channel was reported busy/idle by CCA

Ch. X is free!

Ch. X is free!




o APs gather information of the environment




o Ideally, client STAs too (and report via IEEE 802.11k)

Ch. X is free!

Ch. X is free!




o APs (ideally, STAs too) gather information of the environment




o Distributed approach (autonomous APs)

• Each AP periodically (and asynchronously) scans the medium and chooses the least congested channel local optimum

• Alternatively, APs collaborate (exchange information) to produce better decisions

o Centralized approach (controller-based)

• APs send periodic reports to a controller

– Knowing the whole picture and having more resources (i.e. CPU, memory, etc.) controller runs a sophisticated optimization algorithm global optimum



Other considerations

o Partially overlapping channels

• Chaotic environments (many rogue/unmanaged APs in random channels): take the most of the spectrum by allowing the whole channel set (not only non-overlapping)

o Channel bonding

• 40MHz or 80MHz channels provide higher rates but require more free spectrum not recommended in dense scenarios

o Single Channel Architecture (SCA), aka Channel Blanket

• All APs use the same channel and the same (virtual) BSSID so that all STAs “see” one single AP

– Seamless handover: controller decides AP delivering DL traffic

– Larger collision domain (although DL is scheduled by controller)

© by Extricom

Wi-Fi deployments: load balancing (1)


Wi-Fi users are quasi-static and tend to concentrate in space & time hot spots

o Clients (i.e. traffic) unevenly distributed among APs

• Some APs (channels) congested and some others underutilized

o Load Balancing techniques could increase ability to satisfy QoSrequirements

• Load Balancing techniques widely used in cellular networks

• Take advantage of overlapping areas between neighboring cells

– Clients can be served by several BSs

– System decides the best BS for a client depending on BSs’ loads

• Not directly applicable to Wi-Fi WLANs

– Clients decide association and roaming, not the network

BA C

D E F



Load balancing with client-driven association in WLANs

o Typically, client STAs decide best AP based on RSSI measurements (i.e. strongest Beacon or Probe Response Frame)

• Uneven distribution of users uneven distribution of load

o Some APs broadcast load information (BSS Load element) and some clients do care about it

o Network-oriented client-driven load balancing

• Band steering: encourage utilization of the 5GHz band

– If AP or controller detect a STA sending Probe Requests in the two bands do not send responses through 2.4GHz radios, only through 5GHz

• Disassociation/blacklisting

– Network decides STA’s best AP the rest of APs ignore that STA requests (if already associated, current AP sends Disassociation frame)

• Cell Breathing: adapt size of the cell

– Congested APs reduce tx power of Beacons and Probe Responses underutilized APs do the opposite



Example of cell breathing

o Reduce power of Beacons and Probe Responses

• do not reduce power of data frames since this will reduce suitable rates and increase error rate

Cell A Cell B

1

2

3

4



Example of cell breathing

o Reduce power of Beacons and Probe Responses

• do not reduce power of data frames since this will reduce suitable rates and increase error rate

Cell A Cell B

1

24

3

Wi-Fi deployments: The End


Don’t forget the wires!

o Data/power wires to APs

• If not…multihop or mesh-based wireless distribution system

o Uplink pipe

• Imagine all this headache for just a DSL WAN connection…

Some references (1)

Load balancing

o Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2006, June). “Load Balancing in WLANs through IEEE 802.11k Mechanisms,” in 11th IEEE Symposium on Computers and Communications, ISCC 2006.

o Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2008, July). “Cooperative Load Balancing in IEEE 802.11 Networks with Cell Breathing,” in 13th IEEE Symposium on Computers and Communications, ISCC 2008.

o Garcia-Villegas, E.; Ferrer, JL.; Lopez-Aguilera, E; Vidal, R.; Paradells, J. (2009). “Client-driven load balancing through association control in IEEE 802.11 WLANs”. European Transactions on Telecommunications, ETT vol. 20, no. 5, pp. 494-507. John Wiley & Sons.

Sensitivity control

o Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Smith, G.; Camps-Mur, D. (2015) “Evaluation of Dynamic Sensitivity Control Algorithm for IEEE 802.11ax,” IEEE Wireless Communications and Networking Conference, WCNC 2015, pp. 1072-1077

o Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Camps-Mur, D. (2016) “Dynamic Sensitivity Control Algorithm leveraging adaptive RTS/CTS for IEEE 802.11ax,” in IEEE Wireless Communications and Networking Conference, WCNC 2016

o Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Camps-Mur, D. (2016) “Dynamic Sensitivity Control of Access Points for IEEE 802.11ax”, in IEEE International Conference on Communications, ICC’16


Some references (2)

Channel management

o Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2009). “Frequency assignments in IEEE 802.11 WLANs with efficient spectrum sharing”. Wireless Communications and Mobile Computing, WCMC vol. 9, no. 8, pp. 1125-1140. John Wiley & Sons

o Mengual, E.; Garcia-Villegas, E.; Vidal, R. (2013, September). “Channel management in a campus-wide WLAN with partially overlapping channels,” in The 24th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2013

o Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2011, December). “The Impact of Channel Bonding on 802.11n Network Management,” in 7th International Conference on emerging Networking EXperiments and Technologies, CoNEXT’11

o Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2014). “Intelligent Channel Bonding in 802.11n WLANs,” IEEE Transactions on Mobile Computing, vol. 13, no. 6, pp. 1242-1255

o Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2013, June). “Joint Rate and Channel Width Adaptation for 802.11 MIMO Wireless Networks,” in IEEE Conf. on Sensing, Communication, and Networking, Secon’13, pp. 167-175 (Nominee for the Best Paper Award)

o Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2015). “A practical framework for 802.11 MIMO rate adaptation,” Computer Networks, vol. 83, pp. 332-348


EETAC - UPC

Master's degree in Applied Telecommunications and Engineering Management

IoT & Ubiquitous IP

Course offered at:

http://eetac.upc.edu/en

http://www.upc.edu/

http://eetac.upc.edu/en/masteam



where’s that fu*#}ng wi-fi ?!?...

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